Soil moisture is a crucial parameter in integrated soil sciences, particularly in agriculture. Recognizing its significance, missions such as the NASA SMAP are dedicated to periodically provide global coverage of SM and brightness temperature information. However, the efficacy of satellite measurements hinges on rigorous testing across diverse biomes and conditions.With the objective of creating a SM database that can be used as a reference, in Central Valleys, Oaxaca, monitoring campaigns have been carried out in agricultural areas characterized by their semi-arid climate from 2021 to 2023. The comparison of information is done through a retrieval algorithm known as the τ-ω model that simulates brightness temperature with in-situ information. The study demonstrates how the heterogeneity of the study area and its climate makes the task difficult and causes the need for an adjustment in the in-put parameters.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite aims at estimating surface soil moisture at global scales. In order to validate the soil moisture retrieval algorithms, they need to be tested over different conditions worldwide. In response to this need, the Terrestrial Hydrology Experiment 2021 and 2022 in Mexico (THEXMEX-21 and -22) was conducted in Central Valleys, Oaxaca, Mexico over a twelve-month period. Ground crews collected soil moisture values, crop description, and biomass samples in support of these campaigns. The objective of these field experiments was to create a soil moisture network over a semi-arid agricultural area in Mexico and compare the SMAP soil moisture estimates with soil moisture measurements. The comparison between in-situ values and SMAP retrievals of soil moisture demonstrated that absolute soil moisture values can be delivered by satellite observations. SMAP soil moisture estimates followed the general trend observed during the field experiment.

One of the main socio-environmental problems of the current era is climate change, a problem that has arisen due to anthropogenic actions, which in most cases do not allow ecosystems to recover by leveling the excesses of pollutants. The causes of climate change will be different depending on the natural conditions and society in each region [1] [2]. This work arises from an existing project in both communities where soil moisture and precipitation are monitored during a certain period, with a measurement interval of 20 minutes, so direct measurement equipment is located in the community. Two field visits were made to both communities. The sensitization was carried out first by narrating a story of a small caterpillar that did not make correct use of its available resources.

Agricultural drought periods are a phenomenon that affect crops production and health of ecosystems thereby economies suffer continuous imbalances. For this reason, scientific communities have been focused on accuracy global prediction and mitigation risk models. These models are fed with increasingly enhanced inputs such as TB, therefore the need arises to evaluate inputs. This work presents the relationship between Microwave Polarization Difference Index (MPDI) and Soil Water Deficit Index (SWDI), using TB at 9km of SMAP mission to identify drought periods over a rainfed agricultural area in Huamantla (Tlaxcala State in Central Mexico).

In this study, a data assimilation framework based on the Ensemble Kalman Filter was implemented including a soil-vegetation-atmosphere energy transfer (SVAT) model. The SVAT model has been calibrated with in-situ data in the central region of Mexico, with temperate subhumid climate. The soil moisture information from ten locations was scaled within a 36km satellite pixel. Both synthetic observations and SMAP SM retrieval were assimilated and they improved by 29% compared to open-loop simulations. Particularly, the assimilated soil moisture allows us to have a better characterization of periods of water stress for corn cultivation.

The proper management of resources allows better yields from crops. Within the field of remote sensing, one of the sectors benefited is the agricultural sector because changes in biodiversity can be quantified through the observation and temporary evaluation of satellite images. For example, different studies have shown the potential of using information from the ESA Sentinel-1 satellite to monitor the growing crops. The backscatter observations obtained from Sentinel 1A and Sentinel 1B satellites showed greater sensitivity to the change in vegetation according to the correlation study, it is shown that the correlation between vegetation parameters and Sentinel-1 observations is greater than 0.8. The application of this methodology allows the understanding of the temporal variability over corn fields in the central zone of Mexico and allows seeing the potential of the Sentinel-1 constellation for the monitoring of natural resources. The application of this methodology allows the understanding of the temporal variability over corn fields in the central zone of Mexico and allows seeing the potential of the Sentinel-1 constellation for the monitoring of natural resources.

Since 2018, the Terrestrial Hydrology Experiment in Mexico (THExMEX-18) is being part of SMAP algorithm validation through soil moisture (SM), crop measurements, and biomass samples that were collected over a rainfed agricultural region of Huamantla, Tlaxcala. As a second part of this experiment, THExMEX-19 was carried out in whole crop season from March to December 2019. In order to help to validate to SMAP algorithm applying NASA's protocols, both joint efforts of National Polytechnic Institute and Center for Remote Sensing of the University of Florida have obtained these results in 2018 and 2019. In-Situ soil moisture measurements are compared with the enhanced soil moisture SMAP level 2 passive observation (SMAP L2_SM_P & SMAP L2_SM_P_E) and soil moisture SMAP level 2 and Sentinel-1 passive observation (SMAP L2_SM_SP). SMAP mission meets the requirement of SM retrievals with an unbiased root-mean-square difference (ubRMSD)3m-3) compared to in-situ measurements over agricultural fields. The L-band passive SM retrievals and in-situ measurements were evaluated in terms of four statistical metrics: root-mean-square difference (RMSD), bias, ubRMSD, and correlation coefficient (r).

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For agricultural applications, SM information is needed at higher resolutions (about 1km). In this study, coarse-scale remotely sensed SM at 36 km from NASA-SMAP was disaggregated to 1 km using high resolution auxiliary information such as land cover, precipitation, land surface temperature, NDVI for a growing season of corn in 2018 in Central Mexico (CM). The main objective is to evaluate a machine-learning based downscaling algorithm over an agricultural area with very limited in-situ observations of SM obtained during THExMEX-18. We found that overall, the downscaled moisture captured the dynamics during the growing season observed by the in-situ measurements.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite was launched in January 2015. In order to validate the soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, the algorithms need to be tested over different conditions worldwide. In response to this need, the Terrestrial Hydrology Experiment 2018 in Mexico (THExMEX-18) was conducted in a rainfed agricultural region of Huamantla, Tlaxcala, Mexico over a six-month growing season. During the experiment, soil moisture, crop measurements, and biomass samples were collected. The objective of THExMEX-18 was to create a soil moisture network over an agricultural area in Mexico, understand the spatial distribution of soil moisture, and compare the SMAP soil moisture retrievals with in-situ measurements. This work details the field data collection as well as data calibration and analysis. A first comparison between in-situ data and SMAP retrievals of soil moisture is presented. It is demonstrated that absolute soil moisture values can be retrieved by satellite observations. SMAP soil moisture estimates closely follow dry down and wetting events observed during the field experiment.

Continuous monitoring of physical parameters such as soil moisture (SM) is crucial to improve food sustainability and risks mitigation. Because of the spatio-temporal variations in SM, satellite observations are an excellent tool to monitor its dynamic. Among different operational satellite missions, the NASA SMAP mission offers a unique opportunity to monitor SM worldwide thanks to its frequency band of operation (L-band). The main goal of this mission was the disaggregation of brightness temperature (TB) at 36 km to 9 km using backscatter images at 3 km. Although the SMAP mission was designed to collect simultaneously radar/radiometer observations, due to a failure in the radar in July 2015, only the radiometer continues working. Among different options to compensate the lack of information from the L-band radar, the exploitation of C-band active information with L-band passive observations to disaggregate TBand produce SM at fine resolution has been proposed. Recently, the NASA SMAP team delivered a new SM product using this approach. However, this new product has not been validated over forested areas yet. In this work, we present the results of implementing a disaggregation algorithm based on the baseline SMAP downscaling approach over a tropical forest located in Southern Mexico, using information from the SMAP radiometer and Sentinel-1 images.

The monitoring of tropical ecosystems is complex due to the type of terrain and adverse weather conditions present during most of the year. This paper proposes the development and implementation of a methodology based on RADARSAT-2 images and a semi-empirical model to obtain surface parameters in a tropical forest. The model takes into consideration the sensor configuration, and vegetation and soil parameters to represent the behavior of the wave in the scene. To retrieve the surface parameters, the model is implemented in an optimization framework using all polarizations (HH, HV, VH and VV). Finally, as outputs of the optimization process, we obtain the trunk diameter (Dt), the crow height (hc), and the optical penetration depth (τ). By using τ, the vegetation water content (VWC) is estimated. The accuracy of this methodology is about 83% when compared to ground data.

The volcanic products are deposited in the atmosphere as a result of volcanic eruptions. These materials are dispersed by the wind and can be deposited on the surface in airport facilities and the finest fraction can remain long affecting airspace. Using tools such as wind patterns studio in height and track volcanic clouds with satellite images can identify areas of high probability of being contaminated with ash depending on the season and the intensity of the eruption. In this work two patterns of ash dispersion by wind are identifiedNovember to May transporting the ash has a higher probability NE to the ESE direction. During the months of July to September transporting ash would displace mainly towards the SW to W, with the months of May and October transition.

Forested areas have been the ecosystem more impacted by anthropogenic activities as agriculture, livestock, etc. Particularly, tropical forests host a large diversity of flora and fauna. Southern Mexico, Guatemala, and Belize own the second larger area worldwide of tropical forests after the Amazonas. Despite the importance of this ecosystem, there is still a lack of information, and then, an understanding of the local dynamics. This paper aims at reporting on two field campaigns conducted at Calakmul, Southern, Mexico, to characterize soil and vegetation during rainy and dry seasons. Simultaneously to the field campaigns, Radarsat-2 images were acquired. Significant changes are observed between the rainy and dry seasons; primarily, because of the loss of water content during the dry season for both soil and vegetation.

Volcano monitoring is developed mainly with remote sensing techniques. In the case of SO2, it has been strongly used both surface techniques, such as COSPEC, and satellite techniques, such as MODIS. The two techniques differ in their measurement principle presenting advantages and disadvantages to each other. The purpose of this work is to show the synergy of both remote sensing methods in order to utilize information derived from these two techniques for continuous volcano monitoring. In this communication, we propose a methodology to correct any lag between COSPEC measurements and MODIS images at either time or space based on the wind speed and difference of the exact time of acquisition between the two techniques. The difference observed between COSPEC data and MODIS estimates is removed by using an exponential regression function derived from simultaneous observations using both sensors.

In this paper, we show the application of a coherent model on a tropical forest for studying the backscattering coefficient. The study site is located at southern Mexico. The radar configuration considered is C-band, full polarization and the range of incidence angle is the 20°-50°. As from result obtained we applied the linear regression for a build a simplified model.

Oil industry represents one of the main incomes for many countries. However, this industry is not exempt of accidents such as oil spills. Satellite remote sensing is a useful tool in delineating polluted areas due to its characteristic to cover large areas. This paper proposes a methodology combining information from both optical (Landsat) and radar (Envisat-ASAR) images to delineate polluted soils by hydrocarbons in the coast of Paraiso, Tabasco, Mexico. Landsat images identified polluted areas over the beach (bare soil); nevertheless, they did not reach the soil under vegetation conditions. In contrast, radar images did not indentify polluted areas in the beach mainly because of the extreme dry conditions in the soil. But, in vegetated areas, the radar penetrated the vegetation cover and identified the polluted soils. Both optical and radar information showed complementarities to delineate polluted areas under heterogeneous conditions.